Name
Abstract | ||
|---|---|---|
Evolutionary hypernetworks (EHNs) are recently introduced models for learning higher-order probabilistic relations of data by an evolutionary self-organizing process. We present a method that enables EHNs to learn and generate music from examples. Short-term and long-term sequential patterns can be extracted and combined to generate music with various styles by our method. Based on a music corpus consisting of several genres and artists, an EHN generates genre-specific or artist-dependent music fragments when a fraction of score is given as a cue. Our method shows about 88% of success rate in partial music completion task. By inspecting hyperedges in the trained hypernetworks, we can extract a set of arguments that constitutes melodic structures in music. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/FUZZY.2009.5277047 | FUZZ-IEEE |
Keywords | Field | DocType |
long-term sequential pattern,partial music completion task,evolutionary self-organizing process,trained hypernetworks,music corpus,evolutionary hypernetworks,higher-order probabilistic relation,melodic structure,success rate,artist-dependent music,self organization,higher order,classification algorithms,music,prediction algorithms,data models,data mining,evolutionary computation | Melody,Data modeling,Computer science,Evolutionary computation,Prediction algorithms,Artificial intelligence,Probabilistic logic,Statistical classification,Evolutionary music,Machine learning | Conference |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyun-Woo Kim | 1 | 21 | 6.72 |
| Byoung-Hee Kim | 2 | 20 | 2.72 |
| Byoung-Tak Zhang | 3 | 1571 | 158.56 |